Correcting image blur in medical image

ABSTRACT

A device to correct an image blur within a medical image is described. An image analysis application executed by the device receives the medical image from a medical image provider. Next, the image blur is detected within the medical image by analyzing the medical image. The medical image is subsequently processed with a deep learning model to correct the image blur. In response to the processing, a de-blurred medical image is generated. The de-blurred medical image is provided for a presentation or a continued analysis.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation application relating to and claimingthe benefit of commonly-owned, co-pending PCT International ApplicationNo. PCT/US2019/061464, filed Nov. 14, 2019, entitled “CORRECTING IMAGEBLUR IN A MEDICAL IMAGE,” which claims priority to and the benefit ofcommonly owned U.S. Ser. No. 16/190,652, filed Nov. 14, 2018 entitled“CORRECTING IMAGE BLUR IN MEDICAL IMAGE,” which issued as U.S. Pat. No.10,290,084 on May 14, 2019, the entireties of which are incorporatedherein by reference.

FIELD OF THE EMBODIMENTS

The field of the embodiments relate to a device to correct an image blurwithin a medical image. The corrective mechanism may sharpen blurrededge(s) of an object of interest by processing the medical image with adeep learning model.

BACKGROUND OF THE EMBODIMENTS

Information exchanges have changed processes associated with work andpersonal environments. Automation and improvements in processes haveexpanded the scope of capabilities offered for personal and businessdata consumption. With the development of faster and smallerelectronics, a variety of mobile devices have integrated into dailylives. A modern mobile device includes components to provide variety ofservices such as communication, display, imaging, voice, and/or datacapture, among others. Abilities of the modern mobile device jumpexponentially when networked to other resources that provide previouslyunimagined number of services associated with medical imaging.

Ultrasound and other medical imaging devices remove noise related issuesduring an imaging session by scanning a variety of images of abiological structure of a patient. The scanned images are combined withan averaging process that reduces and/or eliminates noise inherent in animaging session. As an artifact of the averaging process blurringeffects are introduced to the resulting medical image. The blurringeffects may diminish chances of a correct diagnosis that relies ondistinguishable edges associated with an object of interest within themedical image.

SUMMARY OF THE EMBODIMENTS

The present invention and its embodiments relate to a device to correctan image blur in a medical image. The device may be configured toreceive the medical image from a medical image provider. The medicalimage provider may include a medical imaging device. The medical imagemay include an ultrasound scan of a biological structure (such as anorgan) of a patient. Next, the image blur may be detected within themedical image by analyzing the medical image. The image blur may resultfrom a process to reduce noise inherent in the medical imaging processby generating an averaged image of a variety medical ultrasound images(captured during an ultrasound session). Furthermore, the medical imagemay be processed with a deep learning model to correct the image blur.The deep learning model may be generated with a training input set ofaveraged images and an expected output set of de-blurred imagescorresponding to the averaged images. A de-blurred medical image may begenerated in response to processing the medical image. In addition, thede-blurred medical image may be provided for a presentation or acontinued analysis.

In another embodiment of the present invention, a mobile device forcorrecting an image blur in a medical ultrasound image is described. Themobile device may include a memory configured to store instructionsassociated with an image analysis application. A processor may becoupled to the memory. The processor may execute the instructionsassociated with the image analysis application. The image analysisapplication may include a neural network module. The neural networkmodule may be configured to receive the medical ultrasound image from amedical image provider. Next, the image blur may be detected within themedical ultrasound image by analyzing the medical ultrasound image. Theimage blur may result from a noise reduced average of ultrasound sessionimages of a biological structure of a patient. The medical ultrasoundimage may subsequently be processed with a deep learning model tocorrect the image blur. In response to the processing, a de-blurredmedical ultrasound image may be generated. In addition, the de-blurredmedical ultrasound image may be provided for a presentation or acontinued analysis

In yet another embodiment of the present invention, a method ofcorrecting an image blur in a medical ultrasound image is described. Themethod includes receiving the medical ultrasound image from a medicalimage provider. Next, the image blur may be detected within the medicalultrasound image by analyzing the medical ultrasound image. The imageblur may result from a noise reduced average of ultrasound sessionimages of a biological structure of a patient. The medical ultrasoundimage may be processed with a deep learning model to correct the imageblur. In response to the processing, a de-blurred medical ultrasoundimage may be generated. Furthermore, the de-blurred medical ultrasoundimage may be provided for a presentation or a continued analysis.

It is an object of the embodiments of the present invention to correctan image blur in a medical image (such as an ultrasound scan) with aneural network mechanism.

It is an object of the embodiments of the present invention to process amedical image with a deep learning model to detect the image blur.

It is an object of the embodiments of the present invention to processthe medical image with the deep learning model to correct the imageblur.

It is an object of the embodiments of the present invention to sharpenedges of an object of interest in the medical image to correct the imageblur.

These and other features, aspects and advantages of the presentinvention will become better understood with reference to the followingdrawings, description and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a conceptual diagram illustrating examples of correcting animage blur in a medical image, according to an embodiment of theinvention.

FIG. 2 shows a display diagram illustrating components of a neuralnetwork mechanism to correct an image blur in a medical image, accordingto an embodiment of the invention.

FIG. 3 shows another display diagram illustrating components of a neuralnetwork mechanism to correct an image blur in a medical image, accordingto an embodiment of the invention.

FIG. 4 is a block diagram of an example computing device, which may beused to correct an image blur in a medical image.

FIG. 5 is a logic flow diagram illustrating a process for correcting animage blur in a medical image, according to an embodiment of theinvention.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

The preferred embodiments of the present invention will now be describedwith reference to the drawings. Identical elements in the variousfigures are identified with the same reference numerals.

Reference will now be made in detail to each embodiment of the presentinvention. Such embodiments are provided by way of explanation of thepresent invention, which is not intended to be limited thereto. In fact,those of ordinary skill in the art may appreciate upon reading thepresent specification and viewing the present drawings that variousmodifications and variations may be made thereto.

FIG. 1 shows a conceptual diagram illustrating examples of correcting animage blur in a medical image. In an example scenario, a mobile device104 may execute (or provide) an image analysis application 106. Themobile device 104 may include a physical computing device hosting and/orproviding features associated with a client application (such as theimage analysis application 106). The mobile device 104 may includeand/or is part of a smart phone, a tablet based device, and/or a laptopcomputer, among others. The mobile device 104 may also be a node of anetwork. The network may also include other nodes such as the medicalimage provider 112, among others. The network may connect nodes withwired and wireless infrastructure.

The mobile device 104 may execute the image analysis application 106.The image analysis application 106 may receive a medical image 108 froma medical image provider 112. An example of the medical image 108 mayinclude an ultrasound image (or scan). Other examples of the medicalimage 108 may include a x-ray image, a magnetic resonance imaging (MRI)scan, a computed tomography (CT) scan, and/or a positron emissiontomography (PET) scan, among others. The medical image provider 112 mayinclude a medical imaging device/system that captures, manages, and/orpresents the medical image 108 to a user 102. The user 102 may includeas a doctor, a nurse, a technician, a patient, and/or an administrator,among others. The user 102 may use the medical image 108 to diagnose anissue, a malignancy (cancer), and/or other illness associated with apatient.

The medical image 108 and a de-blurred medical image 114 may include anobject of interest (OI) 110. The OI 110 may include a biologicalstructure of a patient. For example, the OI 110 may include a malignantor a benign tumor. Alternatively, the OI 110 may represent anotherstructure associated with an organ and/or other part of the patient.

The image analysis application 106 may next detect an image blur 111within the medical image 108 by analyzing the medical image 108. Theimage blur 111 may result from an averaging process to combine multipleimages captured during an imaging session (such as an ultrasoundsession) of a biological structure of a patient. The medical imagingdevice (conducting the imaging session) may combine the scanned imageswith an averaging process to generate the medical image 108. Theaveraging process may reduce noise inherent in the capture processassociated with the imaging session. However, the averaging process mayblur edge(s) of the biological structure of the patient within themedical image 108. Sharp edges may be critical to automated and/ormanual diagnosis of an illness such as cancer. Blurred edges caused bythe averaging process may hinder attempts at automated and/or manualdiagnosis.

Next, the medical image 108 may be processed with a deep learning modelto correct the image blur 111. The deep learning model may be generatedusing a training input set and an expected output set. The traininginput set may include averaged images (associated with medical imagingsessions) and expected de-blurred images corresponding to the averagedimages.

In response to the processing of the medical image 108, a de-blurredmedical image 114 may be generated. The de-blurred medical image 114 mayinclude the OI 110 with sharpened edges. Subsequently, the de-blurredmedical image 114 may be provided for a presentation to the user 102 ora continued analysis by a downstream analysis application/service.

Previous example(s) to correct an image blur in the medical image 108are not provided in a limiting sense. Alternatively, the image analysisapplication 106 may perform operations associated with correcting theimage blur in the medical image 108 as a desktop application, aworkstation application, and/or a server application, among others. Theimage analysis application 106 may also be a client interface of aserver based application.

The user 102 may interact with the image analysis application 106 with akeyboard based input, a mouse based input, a voice based input, a penbased input, and a gesture based input, among others. The gesture basedinput may include one or more touch based actions such as a touchaction, a swipe action, and a combination of each, among others.

While the example system in FIG. 1 has been described with specificcomponents including the mobile device 104, the image analysisapplication 106, embodiments are not limited to these components orsystem configurations and can be implemented with other systemconfiguration employing fewer or additional components.

FIG. 2 shows a display diagram illustrating components of a neuralnetwork mechanism to correct an image blur 111 in the medical image 108.In an example scenario, the image analysis application 106 (executed bythe mobile device 104) may process the medical image 108 with a neuralnetwork module 216. An example of the medical image 108 may be anultrasound image (or scan). The medical image 108 may also include theOI 110 such as a biological structure of the patient. The medicalimaging device (used to capture the medical image 108) may generate themedical image 108 with an image blur 111. The image blur 111 may softenedge(s) of the OI 110. Sharp edges associated with the OI 110 may becritical to manual or automated diagnosis. As such, the capture processof the medical imaging device may diminish a probability of correctdiagnosis associated with the OI 110.

The capture process may record several images of the OI 110 and combinethe images with an averaging process to generate the medical image 108.The averaging process may remove noise associated with the captureprocess but soften the edges of the OI 110. To enable a correctdiagnosis associated with the OI 110, the image analysis application 106may sharpen edges associated with the OI 110.

Next, the neural network module 216 of the image analysis application106 may process the medical image 108. The neural network module 216 mayprocess the medical image 108 with a deep learning model 218. The deeplearning model 218 may be generated with a training input set 220 and anexpected output set 222. In an example scenario, the image analysisapplication 106 may generate the deep learning model 218. Alternatively,the image analysis application 106 may retrieve the deep learning model218 from an external service provider.

The training input set 220 may include averaged images of prior imagingsessions (from a variety of patients). Each of the averaged images mayinclude a noise reduced average of several medical images (such asultrasound images) captured during an imaging session (such as anultrasound session). Edge(s) of 0I(s) within the averaged images may beblurred as a result of the averaging process to reduce noise.

The expected output set 222 may include de-blurred images correspondingto the averaged images. Edge(s) of the 0I(s) within each of thede-blurred images may be sharpened. The sharpening effect may be appliedautomatically and/or manually to the edge(s). The deep learning model218 may be trained based on an analysis of the training input set 220and the expected output set 222. The training process may form the deeplearning model 218 based on how the sharpening effect is applied to theexpected output set 222 to correct the softened edges of the OIs withinthe training input set 220.

In addition, the neural network module 216 may process the medical image108 with the deep learning model 218 to remove the image blur 111. Theimage blur 111 may be removed by sharpening softened edge(s) of the OI110. As a result of the processing of the medical image 108, the neuralnetwork module 216 may generate a de-blurred medical image 114. Thede-blurred medical image 114 may include the OI with sharpened edge(s).

FIG. 3 shows another display diagram illustrating components of a neuralnetwork mechanism to correct the image blur in the medical image 108.The image analysis application 106 (executed by the mobile device 104)may process the medical image 108 that includes the OI 110. The medicalimage 108 may include the image blur. The image blur may result in theOI 110 having a blurred edge 324. The capture process (to record themedical image 108) may introduce the blurred edge 324 to the OI 110. Thecapture process may combine multiple images recorded during an imagingsession to produce an averaged image (the medical image 108) thatdiminishes noise but softens the OI 110 causing the blurred edge 324.

The neural network module 216 may process the medical image 108 with thedeep learning model 218 to identify and correct the blurred edge 324 ofthe OI 110. In response to processing the medical image 108, the neuralnetwork module 216 may produce the de-blurred medical image 114. Thede-blurred medical image 114 may include the OI 110 with a sharpenededge 326 (among other sharpened edges). The sharpened edge 326 of the OI110 may allow downstream diagnosis process (or a user) to correctlydiagnose a malignancy, an issue and/or an illness associated with the OI110.

The image analysis application 106 may receive the medical image 108from a medical imaging device. Alternatively, the image analysisapplication 106 may receive the medical image 108 from a cameracomponent 325 of the mobile device 104. The camera component 325 mayrecord the medical image 108 as a copy of a scanned image displayed by adisplay device associated with the medical imaging device. The scannedimage may represent an imaging session of a biological structure of apatient (recorded by the medical imagining device).

The medical image 108 may also include a three dimensional image (whichmay represent components of a biological structure of a patient in threedimensions). In another example scenario, the neural network module 216may determine whether the medical image 108 includes a metadata. Inresponse to a verification of the metadata, the neural network module216 may analyze the medical image 108 (to detect the image blur) byevaluating the metadata. For example, the neural network module 216 mayidentify an annotation associated with the medical image 108 within themetadata. The annotation may designate an averaging process used togenerate the medical image from scanned images of a scanning session ofa biological structure of a patient. In response to detecting theannotation, the neural network module 216 may designate the medicalimage 108 as including the image blur.

In yet another example scenario, the image analysis application 106 mayreceive a selection of a region of interest (ROI) of the medical image108 from a user. The neural network module 216 may focus an image blurdetection process and analyze the ROI to identify the image blur withinthe ROI. Furthermore, the medical image 108 may be processed with thedeep learning model 218 in a real-time or offline to remove the imageblur and generate the de-blurred medical image 114 (with the OI 110having the sharpened edge 326). Alternatively, the neural network module216 may process the medical image 108 and subsequent image(s) of a timesequence based scanning session (of a biological structure of a patient)with the deep learning model 218 in a real-time or offline. The neuralnetwork module 216 may generate the de-blurred medical image 114 andde-blurred subsequent image(s) in response to processing of the medicalimage 108 and the subsequent image(s). Alternatively, the neural networkmodule 216 may generate a de-blurred video stream or animation byprocessing the medical image 108 and the subsequent image(s) with thedeep learning model 218.

The example scenarios and schemas in FIGS. 1 through 3 are shown withspecific components, data types, and configurations. Embodiments are notlimited to systems according to these example configurations. A deviceto correct an image blur in the medical image 108 may be implemented inconfigurations employing fewer or additional components in applicationsand user interfaces. Furthermore, the example schema and componentsshown in FIGS. 1 through 3 and their subcomponents may be implemented ina similar manner with other values using the principles describedherein.

FIG. 4 is a block diagram of an example computing device, which may beused to correct an image blur in a medical image, according toembodiments.

For example, computing device 400 may be used as a server, desktopcomputer, portable computer, smart phone, special purpose computer, orsimilar device. In a basic configuration 402, the computing device 400may include one or more processors 404 and a system memory 406. A memorybus 408 may be used for communication between the processor 404 and thesystem memory 406. The basic configuration 402 may be illustrated inFIG. 4 by those components within the inner dashed line.

Depending on the desired configuration, the processor 404 may be of anytype, including but not limited to a microprocessor (μP), amicrocontroller (μC), a digital signal processor (DSP), or anycombination thereof. The processor 404 may include one more levels ofcaching, such as a level cache memory 412, one or more processor cores414, and registers 416. The example processor cores 414 may (each)include an arithmetic logic unit (ALU), a floating-point unit (FPU), adigital signal processing core (DSP Core), a graphics processing unit(GPU), or any combination thereof. An example memory controller 418 mayalso be used with the processor 404, or in some implementations, thememory controller 418 may be an internal part of the processor 404.

Depending on the desired configuration, the system memory 406 may be ofany type including but not limited to volatile memory (such as RAM),non-volatile memory (such as ROM, flash memory, etc.), or anycombination thereof. The system memory 406 may include an operatingsystem 420, the image analysis application 106, and a program data 424.The image analysis application 106 may include components such as theneural network module 216. The neural network module 216 may execute theinstructions and processes associated with the image analysisapplication 106. In an example scenario, the neural network module 216may receive a medical image from a medical image provider. Next, animage blur may be detected within the medical image by analyzing themedical image. The medical image may be processed with a deep learningmodel to correct the image blur. Subsequently, a de-blurred medicalimage may be generated. The de-blurred medical image may be provided fora presentation or a continued analysis.

Input to and output out of the image analysis application 106 may becaptured and displayed through a display component that may beintegrated to the computing device 400. The display component mayinclude a display screen, and/or a display monitor, among others thatmay capture an input through a touch/gesture based component such as adigitizer. The program data 424 may also include, among other data, themedical image 108, or the like, as described herein. The medical image108 may be processed with the deep learning model to correct softenededges of an OI introduced during the imaging process, among otherthings. The computing device 400 may have additional features orfunctionality, and additional interfaces to facilitate communicationsbetween the basic configuration 402 and any desired devices andinterfaces. For example, a bus/interface controller 430 may be used tofacilitate communications between the basic configuration 402 and one ormore data storage devices 432 via a storage interface bus 434. The datastorage devices 432 may be one or more removable storage devices 436,one or more non-removable storage devices 438, or a combination thereof.

Examples of the removable storage and the non-removable storage devicesmay include magnetic disk devices, such as flexible disk drives andhard-disk drives (HDDs), optical disk drives such as compact disk (CD)drives or digital versatile disk (DVD) drives, solid state drives(SSDs), and tape drives, to name a few. Example computer storage mediamay include volatile and nonvolatile, removable, and non-removable mediaimplemented in any method or technology for storage of information, suchas computer-readable instructions, data structures, program modules, orother data.

The system memory 406, the removable storage devices 436 and thenon-removable storage devices 438 are examples of computer storagemedia. Computer storage media includes, but is not limited to, RAM, ROM,EEPROM, flash memory or other memory technology, CD-ROM, digitalversatile disks (DVDs), solid state drives, or other optical storage,magnetic cassettes, magnetic tape, magnetic disk storage or othermagnetic storage devices, or any other medium which may be used to storethe desired information and which may be accessed by the computingdevice 400. Any such computer storage media may be part of the computingdevice 400.

The computing device 400 may also include an interface bus 440 forfacilitating communication from various interface devices (for example,one or more output devices 442, one or more peripheral interfaces 444,and one or more communication devices 466) to the basic configuration402 via the bus/interface controller 430. Some of the example outputdevices 442 include a graphics processing unit 448 and an audioprocessing unit 450, which may be configured to communicate to variousexternal devices such as a display or speakers via one or more A/V ports452. One or more example peripheral interfaces 444 may include a serialinterface controller 454 or a parallel interface controller 456, whichmay be configured to communicate with external devices such as inputdevices (for example, keyboard, mouse, pen, voice input device, touchinput device, etc.) or other peripheral devices (for example, printer,scanner, etc.) via one or more I/O ports 458. An example of thecommunication device(s) 466 includes a network controller 460, which maybe arranged to facilitate communications with one or more othercomputing devices 462 over a network communication link via one or morecommunication ports 464. The one or more other computing devices 462 mayinclude servers, computing devices, and comparable devices.

The network communication link may be one example of a communicationmedia.

Communication media may typically be embodied by computer readableinstructions, data structures, program modules, or other data in amodulated data signal, such as a carrier wave or other transportmechanism, and may include any information delivery media. A “modulateddata signal” may be a signal that has one or more of its characteristicsset or changed in such a manner as to encode information in the signal.By way of example, and not limitation, communication media may includewired media such as a wired network or direct-wired connection, andwireless media such as acoustic, radio frequency (RF), microwave,infrared (IR) and other wireless media. The term computer readable mediaas used herein may include both storage media and communication media.

The computing device 400 may be implemented as a part of a specializedserver, mainframe, or similar computer, which includes any of the abovefunctions. The computing device 400 may also be implemented as apersonal computer including both laptop computer and non-laptop computerconfigurations. Additionally, the computing device 400 may includespecialized hardware such as an application-specific integrated circuit(ASIC), a field programmable gate array (FPGA), a programmable logicdevice (PLD), and/or a free form logic on an integrated circuit (IC),among others.

Example embodiments may also include methods to correct an image blur ina medical image. These methods can be implemented in any number of ways,including the structures described herein. One such way may be bymachine operations, of devices of the type described in the presentdisclosure. Another optional way may be for one or more of theindividual operations of the methods to be performed in conjunction withone or more human operators performing some of the operations whileother operations may be performed by machines. These human operatorsneed not be collocated with each other, but each can be only with amachine that performs a portion of the program. In other embodiments,the human interaction can be automated such as by pre-selected criteriathat may be machine automated.

FIG. 5 is a logic flow diagram illustrating a process for correcting animage blur in a medical image. Process 500 may be implemented on acomputing device, such as the computing device 400 or another system.

Process 500 begins with operation 510, where an image analysisapplication may receive a medical image from a medical image provider.The medical image may include an ultrasound image of a biologicalstructure of a patient. At operation 520, the image blur within themedical image may be detected by analyzing the medical image. Next, atoperation 530, the medical image may be processed with a deep learningmodel to correct the image blur.

Furthermore, at operation 540, a de-blurred medical image may begenerated in response to processing the medical image. The de-blurredmedical image may include sharpened edge(s) of an OI. At operation 550,the de-blurred medical image may be provided for a presentation or acontinued analysis.

The operations included in process 500 is for illustration purposes.Correcting an image blur in a medical image may be implemented bysimilar processes with fewer or additional steps, as well as indifferent order of operations using the principles described herein. Theoperations described herein may be executed by one or more processorsoperated on one or more computing devices, one or more processor cores,specialized processing devices, and/or special purpose processors, amongother examples.

A method of correcting an image blur within a medical image is alsodescribed. The method includes receiving the medical ultrasound imagefrom a medical image provider. Next, the image blur may be detectedwithin the medical ultrasound image by analyzing the medical ultrasoundimage. The image blur may result from a noise reduced average of severalultrasound session images of a biological structure of a patient. Themedical ultrasound image may be processed with a deep learning model tocorrect the image blur. In response to the processing, a de-blurredmedical ultrasound image may be generated. Furthermore, the de-blurredmedical ultrasound image may be provided for a presentation or acontinued analysis.

When introducing elements of the present disclosure or the embodiment(s)thereof, the articles “a,” “an,” and “the” are intended to mean thatthere are one or more of the elements. Similarly, the adjective“another,” when used to introduce an element, is intended to mean one ormore elements. The terms “including” and “having” are intended to beinclusive such that there may be additional elements other than thelisted elements.

Although this invention has been described with a certain degree ofparticularity, it is to be understood that the present disclosure hasbeen made only by way of illustration and that numerous changes in thedetails of construction and arrangement of parts may be resorted towithout departing from the spirit and the scope of the invention.

What is claimed is:
 1. A device to train a deep learning model tocorrect an image blur in a medical image, wherein the device isconfigured to: receive a training input set of a plurality of traininginput medical images and a plurality of expected output medical imagesassociated with the plurality of training input medical images; processthe plurality of training input medical images with a deep learningmodel to correct image blur; generate a plurality of de-blurred traininginput medical images; and train the deep learning model based at leastin part on an analysis of the plurality of de-blurred training inputmedical images and the plurality of expected output medical images. 2.The device of claim 1, wherein each training input medical imageincludes a medical ultrasound image.
 3. The device of claim 1, whereineach training input medical image includes a three dimensional image. 4.The device of claim 1, wherein processing the training input medicalimages further includes a process to: evaluate a metadata of the medicalimage; identify an annotation associated with the medical image withinthe metadata, wherein the annotation designates an averaging processused to generate the medical image from a plurality of scanned images ofa scanning session of a biological structure of a patient.
 5. The deviceof claim 1, wherein processing the training input medical images furtherincludes a process to: receive a selection of a region of interest (ROI)of the medical image from a user; and analyze the ROI to identify theimage blur within the ROI.
 6. The device of claim 1, wherein eachtraining input medical image is processed with the deep learning modelin a real-time or offline.
 7. The device of claim 1, wherein eachtraining input medical image and a subsequent image of a time sequencebased scanning session of a biological structure of a patient areprocessed with the deep learning model in a real-time or offline.
 8. Thedevice of claim 1, wherein the training input set of the deep learningmodel includes averaged images.
 9. The device of claim 8, wherein eachof the training input medical images includes a noise reduced average ofmedical scan images captured during an imaging session.
 10. The deviceof claim 9, wherein one or more edges of an object of interest (OI)within each of the training input medical images is blurred as a resultof the noise reduced average of the medical scan images.
 11. The deviceof claim 8, wherein the expected output set of the deep learning modelincludes de-blurred images corresponding to the averaged images.
 12. Thedevice of claim 11, wherein one or more edges of an object of interest(OI) within each of the de-blurred images are sharpened.
 13. The deviceof claim 1, wherein the image provider includes a medical imagingdevice.
 14. The device of claim 13, wherein the medical imaging deviceis configured to: capture the medical image during a capture session toscan a biological structure of a patient.
 15. The device of claim 1,wherein the image provider includes a camera component.
 16. The deviceof claim 15, wherein the camera component is configured to: capture themedical image from a display device associated with a medical imagingdevice, wherein the display device is configured to display a scannedimage of a biological structure of a patient.
 17. A mobile device fortraining a deep learning model to correcting an image blur in a medicalultrasound image, the mobile device comprising: a memory configured tostore instructions associated with an image analysis application, aprocessor coupled to the display component, the camera component, andthe memory, the processor executing the instructions associated with theimage analysis application, wherein the analysis application includes: aneural network module configured to: receive a training input set of aplurality of training input medical images and a plurality of expectedoutput medical images associated with the plurality of training inputmedical images; process the plurality of training input medical imageswith a deep learning model to correct image blur; generate a pluralityof de-blurred training input medical images; and train the deep learningmodel based at least in part on an analysis of the plurality ofde-blurred training input medical images and the plurality of expectedoutput medical images.
 18. The mobile device of claim 17, whereinprocessing the training input medical images includes one or moreoperations to: identify one or more edges of an object of interest((ill) within the medical ultrasound image, wherein the one or moreedges are blurred by the noise reduced average of the ultrasound sessionimages; and sharpen the one or more edges of the OI based on the deeplearning model.
 19. A method of correcting an image blur in a medicalultrasound image, the method comprising: receiving, by a processor, atraining input set of a plurality of training input medical images and aplurality of expected output medical images associated with theplurality of training input medical images; processing, by a processor,the plurality of training input medical images with a deep learningmodel to correct image blur; generating, by a processor, a plurality ofde-blurred training input medical images; and training, by a processor,the deep learning model based at least in part on an analysis of theplurality of de-blurred training input medical images and the pluralityof expected output medical images.